Abstract: Wi-Fi access point performance management may include receiving a session analytic record related to a plurality of wireless access points, and analyzing the session analytic record related to the plurality of wireless access points to determine a root cause of at least one malfunctioning node related to at least one of the plurality of wireless access points, and predict failure of the at least one of the plurality of wireless access points. Further, a graphical user interface display or a report may be generated. The graphical user interface display or the report may be related to the determination of the root cause of the at least one malfunctioning node related to the at least one of the plurality of wireless access points, and prediction of the failure of the at least one of the plurality of wireless access points.

Abstract: A system for discovery and analysis of network data usage of users of a communication network may collect information related to data usage over a network. The system may determine network data usage patterns for users from the data usage information. The network usage data, usage patterns and additional information may be analyzed to create user segments, and to analyze network data usage for the user segments. Differentiated data services may be created and implemented based on the network data usage for the user segments.

Abstract: Wi-Fi access point performance management may include receiving a session analytic record related to a plurality of wireless access points, and analyzing the session analytic record related to the plurality of wireless access points to determine a root cause of at least one malfunctioning node related to at least one of the plurality of wireless access points, and predict failure of the at least one of the plurality of wireless access points. Further, a graphical user interface display or a report may be generated. The graphical user interface display or the report may be related to the determination of the root cause of the at least one malfunctioning node related to the at least one of the plurality of wireless access points, and prediction of the failure of the at least one of the plurality of wireless access points.

Abstract: In an example, network node failures may be predicted by extracting performance metrics for the network nodes from a plurality of data sources. A fail condition may be defined for the network nodes and input variables related to the fail condition for the network nodes may then be derived from the extracted performance metrics. A plurality of models may then be trained to predict the fail condition for the network nodes using a training set from the extracted performance metrics with at least one of the identified input variables. Each of the plurality of trained models may be validated using a validation set from the extracted performance metrics and may be rated according to predefined criteria. As a result, a highest rated model of the validated models may be selected to predict the fail condition for the network nodes.

Abstract: A system for discovery and analysis of network data usage of users of a communication network may collect information related to data usage over a network. The system may determine network data usage patterns for users from the data usage information. The network usage data, usage patterns and additional information may be analyzed to create user segments, and to analyze network data usage for the user segments. Differentiated data services may be created and implemented based on the network data usage for the user segments.

Abstract: In an example, network node failures may be predicted by extracting performance metrics for the network nodes from a plurality of data sources. A fail condition may be defined for the network nodes and input variables related to the fail condition for the network nodes may then be derived from the extracted performance metrics. A plurality of models may then be trained to predict the fail condition for the network nodes using a training set from the extracted performance metrics with at least one of the identified input variables. Each of the plurality of trained models may be validated using a validation set from the extracted performance metrics and may be rated according to predefined criteria. As a result, a highest rated model of the validated models may be selected to predict the fail condition for the network nodes.